code: def __init__(
      self,
      img_size: int,
      in_channels: int,
      out_channels: int,
      inner_channels: int,
      latent_channels: int,
      channel_multipliers: Tuple[int, ...],
      *,
      num_code: int,
      embedding_channels: int,
      num_res_blocks: int,
      attention_resolutions: Tuple[int, ...] = (),
      dropout: float = 0.0,
      resample_with_conv: bool = True,
      attention_type: str = "spatial",
      apply_tanh: bool = False,
  ):
      super().__init__(
          img_size,
          in_channels,
          out_channels,
          inner_channels,
          latent_channels,
          channel_multipliers,
          embedding_channels=embedding_channels,
          num_res_blocks=num_res_blocks,
          attention_resolutions=attention_resolutions,
          dropout=dropout,
          resample_with_conv=resample_with_conv,
          attention_type=attention_type,
          apply_tanh=apply_tanh,
      )
      self.codebook = VQCodebook(num_code, embedding_channels)
  
imports: import torch
  import torch.nn
  from torch import Tensor
  from typing import Tuple
  from typing import Optional
  from cftool.types import tensor_dict_type
  from .common import IAutoEncoder
  from .common import AutoEncoderInit
  from .common import AutoEncoderLPIPSWithDiscriminator
  from ..general import VQCodebook
  from ...schemas import CustomTrainStepLoss
  from ....schema import IDLModel
  from ....constants import INPUT_KEY
  from ....constants import PREDICTIONS_KEY
  from ....losses.vae import VQVAELoss
label_desc: Use image node_id 1 to create a new AutoEncoderVQ object from inherited base classes: AutoEncoderInit with example: obj = AutoEncoderVQ(img_size, in_channels, out_channels, inner_channels, latent_channels, channel_multipliers)